Modeling the Liquid, Nasal, AND Vowel Transitions OF North American English Using Linear Predictive Filters and Line Spectral Frequency Interpolations for Use in a Speech Synthesis System

A speech synthesis system with an original user interface is being developed. In contrast to most modern synthesizers, this system is not text to speech (TTS). This system allows the user to control vowels, vowel transitions, and consonant sounds through a simple 2-d vowel pad and consonant buttons. In this system, a synthesized glottal waveform is passed through vowel filters to create vowel sounds. Several filters were calculated from recordings of vowels using linear predictive coding (LPC). The rest of the vowels in the North American English vowel space were found using interpolation techniques with line spectral frequencies (LSF). The effectiveness and naturalness of the speech created from transitions between these filters was tested. In addition to the vowel filters, filters for nasal and liquid consonants were found using LPC analysis. Transition filters between these consonants and vowels were determined using LSFs. These transitions were tested as well

Aerodynamic Electrical Energy: Wind Turbine Engineering

Renewable resources such as wind, solar, and water, are important in energy production. This project was to design a wind turbine electricity generation system and gain an understanding of the engineering involved in producing electricity from the wind. Having observed the wind patterns on the campus of Utah State University, it was decided to obtain both a horizontal axis (HAWT) and a vertical axis wind turbine (VAWT) and mount them on the roof of the USU Dean F. Peterson, Jr. Engineering Laboratory Building. The outputs of both turbines were measured and compared. For the low wind conditions of Logan, Utah, the HAWT was found to be the most effective

Airglow-CubeSat with Orientation Control by Aerospike Puff-jets

Observations of upper atmospheric emissions further the understanding of the effects of the chemiluminescent energetics of the Earth’s atmosphere. The Airglow- CubeSat will scan the desired altitudes of the mesosphere and the upper thermosphere. The resulting data is intended to help validate results collected from measurements taken from rocket profiles as well as the SABER/TIMED satellite. The Airglow-CubeSat will be monitoring the atomic oxygen green line at a wavelength of 557 nm. Research is also being conducted into the feasibility of using aerospike technology for altitude maintenance and satellite orientation control

A Nonlinear Latching Filter to Remove Jitter from Movement Estimates for Prostheses

Continuous movement intent decoders are critical for precise control of hand and wrist prostheses. Noise in biological signals (e.g., myoelectric or neural signals) can lead to undesirable jitter in the output of these types of decoders. A low-pass filter (LPF) at the output of the decoder effectively reduces jitter, but also substantially slows intended movements. This paper introduces an alternative, the latching filter (LF), a recursive, nonlinear filter that provides smoothing of small-amplitude jitter but allows quick changes to its output in response to large input changes. The performance of a Kalman filter (KF) decoder smoothed with an LF is compared with that of both an KF decoder without an additional smoother and a KF decoder smoothed with a LPF. These three algorithms were tested in real-time on target holding and target reaching tasks using surface electromyographic signals recorded from 5 non-amputee subjects, and intramuscular electromyographic and peripheral neural signals recorded from an amputee subject. When compared with the LPF, the LF provided a statistically significant improvement in amputee and non-amputee subjects\u27 ability to hold the hand steady at requested positions and achieve movement goals faster. The KF decoder with LF provided a statistically significant improvement in all subjects\u27 ability to hold the prosthetic hand steady, with only slightly lower speeds, when compared to the unsmoothed KF

Individual hand movement detection and classification using peripheral nerve signals

This paper investigates whether the movement intent of an amputee can be detected and classified in real-time as the individual moved his/her phantom hand. We present a method to detect movement intent using neural signals from the peripheral nervous system (PNS). In addition, we classify eight types of individual hand movements using 300 ms signal segments beginning with our detected starting time. Classification is performed by applying linear discriminant analysis (LDA) on different kind of features. We compared the classification results using segments started with the detected starting time and the starting time of the command given to a subject as neural signals were recorded. The average accuracies were 73.5% in the former case and 59.4% in the latter

Recording and Decoding for Neural Prostheses

This paper reviews technologies and signal processing algorithms for decoding peripheral nerve and electrocorticogram signals to interpret human intent and control prosthetic arms. The review includes a discussion of human motor system physiology and physiological signals that can be used to decode motor intent, electrode technology for acquiring neural data, and signal processing methods including decoders based on Kalman filtering and least-squares regressors. Representative results from human experiments demonstrate the progress that has been made in neural decoding and its potential for developing neuroprosthetic arms that act and feel like natural arms